针对品牌定向在线虚假信息的防御策略模拟
Simulating Strategies for Defense Against Brand-Targeted Online Disinformation
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中文总结 AI 辅助
本文通过多智能体模拟研究品牌定向虚假信息的传播与防御，发现无标度网络最脆弱，混合干预策略效果最佳，并提供了可复用的模拟框架。
中文摘要 AI 辅助
品牌定向虚假信息不仅通过传播错误信念损害企业，还会侵蚀信任、加剧声誉不确定性，并在网络化数字环境中持续存在。本文将品牌定向虚假信息建模为一种信任加权传染过程，采用多智能体模拟，涵盖六种网络结构、三种放大水平和六种干预策略。结果表明，无标度网络和以影响力节点为中心的网络最容易受到快速传播的影响，而聚类和极化网络在放大效应跨越社区之前提供部分遏制。结合预驳斥、可信节点激活和快速纠正的混合干预策略整体表现最佳。本文贡献了一个可复用的模拟框架，用于研究主动的品牌虚假信息防御策略。
英文摘要
Brand-targeted disinformation can damage firms not only by spreading false beliefs, but by eroding trust, amplifying reputational uncertainty, and persisting across networked digital environments. This paper models brand-targeted disinformation as a trust-weighted contagion process using a multi-agent simulation across six network structures, three amplification levels, and six intervention strategies. Results show that scale-free and influencer-heavy networks are most vulnerable to rapid spread, while clustered and polarized networks provide partial containment until amplification bridges communities. Hybrid intervention combining prebunking, trusted-node activation, and rapid correction performs best overall. The paper contributes a reusable simulation framework for studying active brand disinformation defense strategies.
发表机构
- Henry W. Bloch School of Management University of Missouri-Kansas City（密苏里大学堪萨斯城分校亨利·W·布洛赫管理学院）
机构由 AI 辅助整理，请以论文原文为准。
